A method for estimating the line-of-sight angular rate of a missile based on forecast information

Abstract

The application provides a two-step filtering design method for estimating the line-of-sight angular rate of a missile-target. The method applies the information of the missile-target obtained by ground tracking and the target maneuvering acceleration information obtained by passive tracking to realize two-step filtering and thus line-of-sight angular rate extraction. For a large maneuvering target, the method firstly estimates the target acceleration through a jerk model, and then brings the acceleration into a six-dimensional model to estimate the line-of-sight angular rate. The simulation results show that the method can realize high-precision extraction of the line-of-sight angular rate, and greatly improves the interception performance of the interception system on the maneuvering target.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of tracker guidance control, and particularly relates to a two-step filter design method for estimating a line-of-sight angular rate of a missile-target. BACKGROUND

[0002] The line-of-sight angular rate is used to describe the change rate of a missile-target connecting line relative to an inertial space, and the precision thereof can determine the success or failure of an interception task. Therefore, according to the related measurement information that can be obtained by a tracker, it is a key link of the interception task to extract a high-precision line-of-sight angular rate of a missile-target. A tracker seeker can only measure the line-of-sight angle information of a target relative to a missile body, which is coupled with the attitude information of the missile body, and further has a corresponding complex dynamic coupling effect caused by the influence of the attitude and orbit control engine, which brings great difficulty to the extraction of the line-of-sight angular rate. SUMMARY

[0003] The application aims at solving the problems in the prior art, and provides a two-step filter design method for estimating a line-of-sight angular rate of a missile-target.

[0004] The application is implemented by the following technical scheme, and provides a two-step filter design method for estimating a line-of-sight angular rate of a missile-target, which comprises the following steps:

[0005] Step one: 'first-step' filtering, a Jerk model and an extended Kalman filter are combined to estimate the target acceleration through the measured target position and velocity information;

[0006] Step two: according to the position relationship between the target and the tracker, a relative motion dynamics equation of the tracker and the target in a line-of-sight coordinate system is derived;

[0007] Step three: the 'first-step' filtering result and the dynamics equation are combined, a 6-order maneuvering model of the target is established by selecting appropriate state variables;

[0008] Step four:'second-step' filtering, based on the 6-order maneuvering model, a tracking filter model is established, an extended Kalman filter is designed through the relative distance, relative velocity and line-of-sight angle information measured by the seeker, and the estimation of the line-of-sight angular rate is realized;

[0009] Step five: two-step filtering is integrated, and the estimated line-of-sight angular rate is used for guidance simulation.

[0010] Further, in step one, the Jerk model is used to respectively describe the change of the target jerk components on the three axes of the launch point inertial coordinate system; and the dynamic model of the interception system is

[0011] Further, in step one, the Jerk model is used to respectively describe the change of the target jerk components on the three axes of the launch point inertial coordinate system; and the dynamic model of the interception system is

[0012] where,

[0013]

[0014] x(t),y(t),z(t) represent the three components of the relative position between the target and the tracker in the rectangular coordinate system; v x (t),v y (t),v z (t) represent the three components of the relative velocity between the target and the tracker in the rectangular coordinate system; and a tx (t),a ty (t),a tz (t) represent the three components of the target acceleration in the rectangular coordinate system; represent the three components of the target jerk in the rectangular coordinate system;

[0015]

[0016] Each of the parts A and B represents a 3x3 matrix; λ t is given by

[0017]

[0018] λ is the inverse of the target acceleration time constant in the jerk model;

[0019] After discretizing equation (1), we have

[0020] x(t+1) = Φx(t) + u(t) (2)

[0021] where,

[0022]

[0023] where Δt represents the measurement period;

[0024] The position and velocity information in three directions can be measured by the seeker; the measurement equation is

[0025] η x = [x v x y v y z v z ]

[0026] The measurement matrix is:

[0027]

[0028] According to the linear state prediction equation (2) and the linear measurement equation, the Kalman filter can be constructed,

[0029] Its prediction equation is

[0030]

[0031] where Q is the model prediction error covariance matrix; the measurement update equation of the filter is

[0032]

[0033] The acceleration of the target is estimated.

[0034] Further, in step two, the target-tracker line-of-sight vector is represented as

[0035]

[0036] where and represent the position vectors of the target and tracker in the inertial coordinate system, respectively; the above equation is differentiated with respect to time to obtain

[0037]

[0038] where and represent the time derivatives of the line-of-sight vector in the inertial coordinate system and the line-of-sight coordinate system, respectively, represents the angular velocity of the line-of-sight coordinate system relative to the inertial coordinate system, and represent the velocity vectors of the target and tracker, respectively; the above equation is written in the projection form in the line-of-sight coordinate system:

[0039]

[0040] where

[0041]

[0042] then

[0043]

[0044]

[0045] The formula

[0046]

[0047] is differentiated with respect to time to obtain

[0048]

[0049] Projecting to the line-of-sight coordinate system, we obtain

[0050]

[0051] i.e.

[0052]

[0053] where a r , a ε and a β are the components of the tracker acceleration in the LOS coordinate system; a tr , a tε and a tβ are the components of the target acceleration in the LOS coordinate system.

[0054] Further, in step three, substituting equation (10) into equation (13) gives

[0055]

[0056] where v r , v ε and v β are the components of the tracker relative velocity vector in the LOS coordinate system; the state variable is chosen as:

[0057] X = [r v r q ε v ε q β v β ] T (15)

[0058] From the above, the 6th order maneuver model can be obtained as:

[0059]

[0060] The relative distance r, the relative velocity the elevation angle q ε and the azimuth angle q β are chosen as the measurements; a tr , a tε and a tβ are the accelerations estimated from the Jerk model and are obtained by coordinate transformation.

[0061] Further, in step four, from equation (5) we have According to the seeker measurement information, the relative distance r, the relative velocity the elevation angle q ε and the azimuth angle q β are measured, thus the measurement matrix is:

[0062]

[0063] From the above, the model of the tracking filter is obtained as:

[0064]

[0065] where X∈R 6 and Y∈R 4 are the measurement outputs of the filter, W and V are system noise and measurement noise, and both are Gaussian white noise.

[0066] Further, the steps of designing the extended Kalman filter according to the tracking filter model are specifically as follows:

[0067] Firstly, linearize the tracking filter model to obtain the Jacobian matrix φ k :

[0068]

[0069] Secondly, predict the prior estimate of the state x k+1|k :

[0070] x k+1|k = f(x k|k ) (19)

[0071] Thirdly, predict the prior estimate of the covariance matrix P k+1|k :

[0072] P k+1|k = φ k P k|k φ k T + Q (20)

[0073] Fourthly, calculate the state gain matrix K k+1 :

[0074] K k+1 = P k+1|k H k+1 T (H k+1 P k+1|k H k+1 T + R) -1 (21)

[0075] Fifthly, correct the state gain matrix to obtain the posterior estimate of the state x k+1|k+1 :

[0076] x k+1|k+1 = x k+1|k + K k+1 (z k+1 -Hx k+1|k ) (22)

[0077] Sixth step, according to the state gain matrix correction covariance posteriori estimation value P k+1|k+1 .

[0078] Further, the covariance posteriori estimation value P k+1|k+1 For:

[0079] P k+1|k+1 =(I-K k+1 H)P k+1|k (23).

[0080] The application further provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the "two-step" filter design method for estimating the line-of-sight angular velocity of a projectile and target when executing the computer program.

[0081] The application further provides a computer readable storage medium for storing computer instructions, wherein the computer instructions implement the steps of the "two-step" filter design method for estimating the line-of-sight angular velocity of a projectile and target when executed by a processor.

[0082] The application has the following beneficial effects:

[0083] The application provides a "two-step" filter design method for estimating the line-of-sight angular velocity of a projectile and target, which comprehensively applies the projectile information obtained by "ground tracking" and the target maneuvering acceleration information obtained by "passive tracking" to perform "two-step" filtering to realize line-of-sight angular velocity extraction. For a large maneuvering target, the method estimates the target acceleration through a jerk model first, and then brings the target acceleration into a six-dimensional model to estimate the line-of-sight angular velocity. Simulation results show that the method can realize high-precision extraction of the line-of-sight angular velocity, and greatly improves the interception performance of an interception system on a maneuvering target. BRIEF DESCRIPTION OF DRAWINGS

[0084] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of the provided drawings.

[0085] Figure 1 is a schematic diagram of y-axis direction acceleration estimation;

[0086] Figure 2 is a schematic diagram of z-axis direction acceleration estimation;

[0087] Figure 3 is a schematic diagram of high-low angle line-of-sight angular velocity estimation;

[0088] Figure 4 is a schematic diagram of the azimuth line-of-sight angular rate estimation case;

[0089] Figure 5 is a schematic diagram of the high-low line-of-sight angular rate estimation error;

[0090] Figure 6 is a schematic diagram of the azimuth line-of-sight angular rate estimation error;

[0091] Figure 7 is a schematic diagram of the miss distance in 100 Monte Carlo simulation experiments. DETAILED DESCRIPTION

[0092] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0093] In combination with Figures 1-7 , the present application proposes a two-step filtering design method for estimating the line-of-sight angular rate of a projectile-target, which comprises the following steps:

[0094] Step one: 'first step' filtering, in combination with the Jerk model and the extended Kalman filter, target acceleration estimation is performed through the measured target position and velocity information;

[0095] In step one, the Jerk model is used to describe the change of the target jerk component on the three axes of the launch point inertial coordinate system; the dynamic model of the interception system is

[0096]

[0097] wherein,

[0098]

[0099] x(t), y(t), z(t) represent three components of the relative position between the target and the tracker in the rectangular coordinate system; v x (t), v y (t), v z (t) represent three components of the relative velocity between the target and the tracker in the rectangular coordinate system; and a tx (t), a ty (t), a tz (t) represent three components of the target acceleration in the rectangular coordinate system; represent three components of the target jerk in the rectangular coordinate system;

[0100]

[0101] Each of the parts A and B represents a 3x3 matrix; λ t is given by

[0102]

[0103] λ is the inverse of the target acceleration time constant in the Jerk model;

[0104] After discretization of equation (1) we obtain

[0105] x(t+1) = Φx(t) + u(t) (2)

[0106] where,

[0107]

[0108] where Δt denotes the measurement period;

[0109] The position and velocity information in three directions can be measured by the seeker; the measurement equation is

[0110] η x = [x v x y v y z v z ]

[0111] The measurement matrix is:

[0112]

[0113] According to the linear state prediction equation (2) and the linear measurement equation, the Kalman filter can be constructed,

[0114] The prediction equation of which is

[0115]

[0116] where Q is the model prediction error covariance matrix; the measurement correction equation of the filter is

[0117]

[0118] From which the acceleration of the target is estimated.

[0119] Step two: according to the position relationship between the target and the tracker, the relative motion dynamics equation of the tracker and the target in the line-of-sight coordinate system is derived;

[0120] In step two, the target-tracker line-of-sight vector is represented as

[0121]

[0122] where, and are the position vectors of the target and tracker in the inertial frame, respectively; the above equations are differentiated with respect to time, to obtain

[0123]

[0124] where, and are the derivatives of the line-of-sight vector with respect to time in the inertial frame and line-of-sight frame, respectively, is the angular velocity of the line-of-sight frame with respect to the inertial frame, and are the velocity vectors of the target and tracker, respectively; the above equations are written in the line-of-sight frame in the form of projections:

[0125]

[0126] where,

[0127]

[0128] then

[0129]

[0130] Differentiating equation (1) with respect to time, we obtain

[0131]

[0132] Differentiating equation (2) with respect to time, we obtain

[0133]

[0134] Projecting into the line-of-sight frame, we obtain

[0135]

[0136] i.e.

[0137]

[0138] where, a r , a ε and a β are the components of the acceleration of the tracker in the line-of-sight frame, and a tr , a tε and a tβ are the components of the acceleration of the target in the line-of-sight frame.

[0139] Step three: combine the results of the first step filtering and the dynamic equation, select appropriate state variables to establish the target 6-order maneuvering model;

[0140] In step three, formula (10) is brought into formula (13) to obtain

[0141]

[0142] Where, v r , v ε and v β are the components of the relative speed vector of the tracker in the three axes of the line-of-sight coordinate system; the state variables are selected as:

[0143] X = [r v r q ε v ε q β v β ] T (15)

[0144] Based on the above, the 6-order maneuvering model can be obtained:

[0145]

[0146] The relative distance r, the relative speed the elevation angle q ε and the azimuth angle q β are selected as the measurement quantities; a tr , a tε and a tβ are the accelerations estimated by the jerk model and obtained through coordinate conversion.

[0147] Step four: the second step filtering, based on the 6-order maneuvering model, the model of the tracking filter is established, the relative distance, the relative speed and the line-of-sight angle information measured by the seeker are used to design the extended Kalman filter, and the line-of-sight angle rate is estimated;

[0148] In step four, it can be known from formula (5) that According to the measurement information of the seeker, the relative distance r, the relative speed the elevation angle q ε and the azimuth angle q β are measured, and thus the measurement matrix is:

[0149]

[0150] The model of the tracking filter can be obtained by comprehensively considering the above as:

[0151]

[0152] Where, X ∈ R 6and Y e R 4 is the measurement output of the filter, W and V are system noise and measurement noise, and both are Gaussian white noise.

[0153] The steps of designing the extended Kalman filter according to the tracking filter model are as follows:

[0154] Firstly, linearize the tracking filter model to obtain the Jacobian matrix φ k :

[0155]

[0156] H itself is linear, and does not need to be Taylor expanded.

[0157] Secondly, predict the prior estimate of the state x k+1|k :

[0158] x k+1|k = f(x k|k ) (19)

[0159] Thirdly, predict the prior estimate of the covariance matrix P k+1|k :

[0160] P k+1|k = φ k P k|k φ k T + Q (20)

[0161] Fourthly, calculate the state gain matrix K k+1 :

[0162] K k+1 = P k+1|k H k+1 T (H k+1 P k+1|k H k+1 T + R) -1 (21)

[0163] Fifthly, correct the state gain matrix to obtain the posterior estimate of the state x k+1|k+1 :

[0164] x k+1|k+1 = x k+1|k + K k+1 (z k+1 -Hx k+1|k ) (22)

[0165] Sixthly, correct the state gain matrix to obtain the posterior estimate of the covariance P k+1|k+1 .

[0166] The covariance posterior estimate P k+1|k+1 is:

[0167] P k+1|k+1 = (I - K k+1 H)P k+1|k (23).

[0168] Step five: integrate the "two-step" filter, and use the estimated line-of-sight angular rate to guide the simulation.

[0169] Simulation part

[0170] To verify the effectiveness of the 6-order maneuver model proposed in the present application for line-of-sight angular rate extraction, a reentry strong maneuver target is simulated, with a maneuver frequency of 0.5 Hz and a maximum maneuver acceleration of a max = ± 20g for a spiral maneuver.

[0171] It is assumed that the initial missile-target distance is R = 13900 m, the initial speed of the target is v xt = -1.28 x 10 3 m / s, v yt = -1.49 x 10 3 m / s, v zt = 0 m / s, and the initial speed of the tracker is v x = 1.026 x 10 3 m / s, v y = 1.117 x 10 3 m / s, v z = 0 m / s. The measurement error of the missile-target distance R is 3σ = 30 m, the measurement error of the relative speed V is 3σ = 2 m / s, the measurement error of the line-of-sight angle q ε and q β is σ = 0.01°, and thus the measurement noise variance matrix R n and the system noise variance matrix are

[0172] Q n = diag [Q1 Q2... Q6]

[0173] wherein,

[0174] Q1 = 10^(-6), Q2 = 10^(-6), Q3 = 10^(-14), Q4 = 10^(-2), Q5 = 10^(-14), Q6 = 10^(-2),

[0175] Through simulation, it can be concluded that when the maximum overload is 20g and the maneuvering frequency is 0.5Hz, the accuracy of the acceleration estimation is higher by using the two-step filtering design method proposed in the application, and the estimated line-of-sight angular rate error is less than 0.1° / s in the high-low angle and azimuth angle directions; through 100 times of Monte Carlo simulation experiments, the miss distance is within 0.06m, and the average miss distance is 0.03m, the strong maneuvering target can be effectively intercepted, and the application has important engineering application value and lays a foundation for the design of a guidance law.

[0176] The application further provides an electronic device, including a memory and a processor, the memory stores a computer program, and the processor realizes the steps of the two-step filtering design method for estimating the line-of-sight angular rate of a missile-target when executing the computer program.

[0177] The application further provides a computer readable storage medium for storing computer instructions, and the computer instructions realize the steps of the two-step filtering design method for estimating the line-of-sight angular rate of a missile-target when executed by a processor.

[0178] The memory in the embodiments of the application can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. The non-volatile memory can be a read only memory (ROM), a programmable ROM (PROM), an erasable programmable ROM (EPROM), an electrically EPROM (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM) used as an external cache. By way of example, and not limitation, many forms of RAM can be used, such as a static RAM (SRAM), a dynamic RAM (DRAM), a synchronous dynamic RAM (SDRAM), a double data rate SDRAM (DDR SDRAM), an enhanced SDRAM (ESDRAM), a synchlink DRAM (SLDRAM), and a direct rambus RAM (DRRAM). It is to be noted that the memory of the method described in the application is intended to include, but not be limited to, these and any other suitable types of memory.

[0179] In the above embodiments, all or part of the methods can be implemented by software, hardware, firmware, or any combination thereof. When implemented by software, all or part of the methods can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center through wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that includes one or more available media sets. The available media can be magnetic media (such as floppy disk, hard disk, magnetic tape), optical media (such as high-density digital video disc (DVD)), or semiconductor media (such as solid state disc (SSD)), etc.

[0180] In the implementation process, each step of the above method can be completed by integrated logic circuit of hardware in the processor or instruction in the form of software. The steps of the method disclosed in the embodiments of the present application can be directly embodied as hardware processor execution, or executed by combination of hardware and software modules in the processor. The software module can be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, register, and other mature storage media in the art. The storage medium is located in the memory, and the processor reads the information in the memory and combines the hardware to complete the steps of the above method. To avoid repetition, it will not be described in detail here.

[0181] It should be noted that the processor in the embodiments of the present application can be an integrated circuit chip with a signal processing capability. In the implementation process, each step of the method embodiments can be completed by the integrated logic circuit of hardware or the instruction in the form of software in the processor. The processor mentioned above can be a general processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The disclosed methods, steps and logic block diagrams in the embodiments of the present application can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as a hardware code processor for execution, or a combination of hardware and software modules in the code processor for execution. The software module can be located in a random access memory, a flash memory, a read only memory, a programmable read only memory or an electrically erasable programmable memory, a register or other mature storage medium in the art. The storage medium is located in the memory, and the processor reads the information in the memory, and combines the hardware to complete the steps of the above method.

[0182] The above describes in detail the "two-step" filter design method for estimating the line-of-sight angular rate of a missile. The principles and implementation methods of the present application are described by using specific examples. The above description of the embodiments is only used to help understand the method of the present application and its core idea. Meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation methods and application scope will be changed. In summary, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A "two-step" filter design method for estimating the missile-target line-of-sight angular rate, characterized in that, The method comprises the following steps: Step one: 'first step' filtering, combining the Jerk model and the extended Kalman filter, estimating the target acceleration through the measured target position and velocity information; Step two: according to the position relationship between the target and the tracker, deducing the relative motion dynamics equation of the tracker and the target in the line-of-sight coordinate system; Step three: combining the 'first step' filtering result and the dynamics equation, selecting appropriate state variables to establish a 6-order maneuvering model of the target; Step four:'second step' filtering, based on the 6-order maneuvering model, establishing a tracking filter model, through the measured relative distance, relative velocity and line-of-sight angle information of the seeker, designing the extended Kalman filter to realize the estimation of the line-of-sight angular rate; Step five: integrating the 'two-step' filtering, using the estimated line-of-sight angular rate to perform guidance simulation; In step one, the Jerk model is used to describe the change of the target jerk component on the three axes of the launch point inertial coordinate system; the dynamic model of the interceptor system is (1) Wherein, three components in the rectangular coordinate system representing the relative position between the target and the tracker; three components in the rectangular coordinate system representing the relative velocity between the target and the tracker; and three components in the rectangular coordinate system representing the target acceleration; three components in the rectangular coordinate system representing the target jerk; , Each of the parts in A and B represents a 3 x 3 matrix; is given by the formula is the inverse of the target acceleration time constant in the Jerk model; After discretizing formula (1), the following formula can be obtained (2) Wherein, (3) wherein denotes the measurement period; The position information and velocity information in three directions can be measured through the seeker; therefore, the measurement is The measurement matrix is: According to the linear state prediction equation (2) and the linear measurement equation, the Kalman filter can be constructed, The prediction equation is (4) where is the model prediction error covariance matrix; and the measurement update equation of the filter is (5) Thus, the target acceleration is estimated.

2. The method of claim 1, wherein, In step two, the target-tracker line-of-sight vector is represented as (6) where and represent the position vectors of the target and tracker in the inertial frame; differentiating the above equations with respect to time gives (7) where and denote the time derivative of the line-of-sight vector with respect to time in the inertial and line-of-sight coordinate systems, respectively, denotes the angular velocity of the line-of-sight coordinate system with respect to the inertial coordinate system, and denote the target and tracker velocity vectors, respectively; the above equations are written in the projected form in the line-of-sight coordinate system: (8) Wherein, ， (9) is a high-low angle, is an azimuth angle; is a relative velocity; Therefore, ＝ wherein is the relative distance; Deriving formula (10) with respect to the relative time, the following formula is obtained (11) After being projected to the line-of-sight coordinate system, the following formula is obtained (12) That is (13) wherein, , and are the components of the tracker acceleration on the three axes of the line-of-sight coordinate system, , and are the components of the target acceleration on the three axes of the line-of-sight coordinate system.

3. The method of claim 2, wherein, In step three, formula (10) is brought into formula (13) to obtain (14) where, , and are the components of the tracker relative velocity vector on the three axes of the line-of-sight coordinate system; select the state variables: (15) Therefore, the 6-order maneuvering model can be obtained: (16) The relative distance , the relative velocity , the high-low angle and the azimuth angle are selected as the measurement quantities; , The acceleration calculated from the Jerk model is converted to the coordinate system to obtain the acceleration and .

4. The method of claim 3, wherein, In step four, from formula (5) , according to the seeker measurement information, the relative distance , the relative velocity , the high-low angle and the azimuth angle are measured, thus the measurement matrix is: According to the tracking filter model, the steps of designing the extended Kalman filter are as follows: (17) wherein and is the measured output of the filter, and are system noise and measurement noise, both of which are Gaussian white noise.

5. The method of claim 4, wherein, The processor executes the computer program to realize the steps of the method in any one of claims 1-6. First, the tracking filter model is linearized to obtain the Jacobian matrix : (18) Second step, prediction of the state prior estimate : (19) Step 3, predict a prior estimate of the covariance matrix : (20) Step 4. Calculate the state gain matrix according to the obtained prior estimate value : (21) Step 5. State posterior estimate is obtained by correcting the state gain matrix : (22) Step 6. Covariance posterior estimate is obtained by correcting the state gain matrix .

6. The method of claim 5, wherein, The covariance posterior estimate is: (23)。 7.An electronic device comprising a memory and a processor, the memory storing a computer program, wherein, The computer instructions are executed by the processor to realize the steps of the method in any one of claims 1-6.

8. A computer readable storage medium for storing computer instructions, characterized in that, ​

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